Synergetic Development Measure of Airport Groups Composite System and Its Influencing Factors Analysis: Some Evidence from China
Bibliographic record
Abstract
Whether airport groups develop synergistically has become a concern for the aviation industry. Existing studies have neglected the dynamic comprehensive evaluation and analysis of influencing factors of synergy degree. Taking the synergistic development of airport groups as the research perspective, we integrate the dynamic comprehensive evaluation model into the traditional synergy model. From two dimensions of airport development and regional economy, we measured the static synergy degree of the Chengdu-Chongqing airport group and the dynamic synergetic development degree on the overall time series. Further, using a spatial econometric model, we construct a distance economy-nested spatial weight matrix so as to explore the influence of each indicator on the synergy degree of the composite system. The results show that the overall synergy of the Chengdu-Chongqing airport group is above 0.5 in terms of static synergy degree. The clustering spectrum shows that the Chengdu-Chongqing airport group can be divided into three categories and basically forms the characteristic of “two main and multiple auxiliary airports.” In terms of dynamic synergetic development degree and comprehensive evaluation value, the change speed of synergy degree is relatively smooth in the early stage and fluctuates in the later stage, and the overall dynamic evaluation value of the Chongqing regional airports is high. The effects of the explanatory variables of the airport group composite system on the synergy degree show more direct effects than spillover effects, and the direct effects are all positive feedback effects, while the negative spillover effects of the explanatory variables cannot be ignored.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".